Generating Alternatives Using Simulation-Optimization Combined with Niching Operators to Address Unmodelled Objectives in a Waste Management Facility Expansion Planning Case

Julian Scott Yeomans and Yavuz Gunalay
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Julian Scott Yeomans: Department of Operations Management & Information Systems, York University, Toronto, ON, Canada
Yavuz Gunalay: Department of Economic and Administrative Sciences, Bahcesehir University, Istanbul, Turkey

International Journal of Operations Research and Information Systems (IJORIS), 2013, vol. 4, issue 2, 50-68

Abstract: Public sector decision-making typically involves complex problems that are riddled with incompatible performance objectives and possess competing design requirements which are very difficult – if not impossible – to quantify and capture when supporting decision models need to be constructed. There are invariably unmodelled design issues, not apparent at the time of model creation, which can greatly impact the acceptability of the solutions proposed by the model. Consequently, it is generally preferable to create several quantifiably good alternatives that provide multiple, disparate perspectives and very different approaches to the particular problem. These alternatives should possess near-optimal objective measures with respect to the known modelled objective(s), but be fundamentally different from each other in terms of the system structures characterized by their decision variables. By generating a set of very different solutions, it is hoped that some of the dissimilar alternatives can provide very different perspectives that may serve to satisfy the unmodelled objectives. This study shows how simulation-optimization (SO) modelling can be combined with niching operators to efficiently generate multiple policy alternatives that satisfy required system performance criteria in stochastically uncertain environments and yet are maximally different in the decision space. This new stochastic approach is very computationally efficient, since it permits the simultaneous generation of good solution alternatives in a single computational run of the SO algorithm. The efficacy and efficiency of this modelling-to-generate-alternatives (MGA) method is specifically demonstrated on a municipal solid waste management facility expansion case.

Date: 2013
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